Results 11 to 20 of about 374,385 (274)

Hierarchical Gaussian process mixtures for regression [PDF]

Statistics and Computing, 2004
As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields.
A. Gelman, A. O?Hagan, A.M. Horowitz, B. Cheng, B.P. Carlin, D.M. Titterington, D.M. Titterington, G.J. McLachlan, J.O. Ramsay, J.Q. Shi, M. Stephens, R. Kamnik, R. Murray-Smith, S. Duane, S. Geman, S. Richardson, T.J. Thompson, V. Tresp   +17 more
core   +5 more sources

Efficient inference of synaptic plasticity rule with Gaussian process regression [PDF]

iScience, 2023
Summary: Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings.
Shirui Chen, Qixin Yang, Sukbin Lim
doaj   +2 more sources

Gaussian Process Regression with Mismatched Models [PDF]

, 2001
Learning curves for Gaussian process regression are well understood when the `student' model happens to match the `teacher' (true data generation process). I derive approximations to the learning curves for the more generic case of mismatched models, and
Sollich, Peter
core   +5 more sources

Efficiency improvement of spin-resolved ARPES experiments using Gaussian process regression [PDF]

Scientific Reports
The experimental efficiency has been a central concern for time-consuming experiments. Spin- and angle-resolved photoemission spectroscopy (spin-resolved ARPES) is renowned for its inefficiency in spin-detection, despite its outstanding capability to ...
Hideaki Iwasawa, Tetsuro Ueno, Takuma Iwata, Kenta Kuroda, Konstantin A. Kokh, Oleg E. Tereshchenko, Koji Miyamoto, Akio Kimura, Taichi Okuda   +8 more
doaj   +2 more sources

Modeling forecast errors for microgrid operation using Gaussian process regression [PDF]

Scientific Reports
Microgrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators.
Yeuntae Yoo, Seungmin Jung
doaj   +2 more sources

Lossy compression of observations for Gaussian process regression [PDF]

MATEC Web of Conferences, 2022
This paper proposes a novel approach of Gaussian process observation set compression based on a squared difference measure. It is used to discard observations to speed up Gaussian process prediction while retaining the information encoded in the full set
Visser Emile, van Daalen Corné E., Schoeman J. C.   +2 more
doaj   +1 more source

Sustainable Dyeing Process Modeling for Recycled PET/PCT Microfibers via Gaussian Process Regression [PDF]

ACS Omega
Hyeokjun Cho, Seung Geol Lee
doaj   +2 more sources

Manifold Gaussian Processes for regression [PDF]

2016 International Joint Conference on Neural Networks (IJCNN), 2016
26.03.14 KB.
Roberto Calandra, Jan Peters 0001, Carl Edward Rasmussen, Marc Peter Deisenroth   +3 more
openaire   +5 more sources

Non-Gaussian Process Regression

CoRR, 2022
Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outliers and shocks routinely observed. In these cases GPs can fail to model uncertainty adequately and may over-smooth inferences.
Yaman Kindap, Simon J. Godsill
openaire   +2 more sources

Complex Gaussian Processes for Regression [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2018
In this paper, we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one. This approach is limited. Based on the results in complex-valued linear theory and Gaussian random processes,
Rafael Boloix-Tortosa, Juan José Murillo-Fuentes, F. Javier Payan-Somet, Fernando Pérez-Cruz   +3 more
openaire   +4 more sources